Add sequence mask for grpo

tests/algorithm/policy_loss_test.py

@@ -116,6 +116,33 @@ def test_mix_policy_loss(self):
         self.assertTrue(torch.allclose(torch.tensor(metrics["expert/sft_loss"]), sft_loss))
         self.assertTrue(torch.allclose(torch.tensor(metrics["loss"]), mix_loss))
 
+    def test_ppo_policy_loss_with_sequence_masking(self):
+        policy_loss_fn_cls = POLICY_LOSS_FN.get("ppo")
+        policy_loss_fn_args = policy_loss_fn_cls.default_args()
+        policy_loss_fn_args["enable_sequence_masking"] = True
+        policy_loss_fn_args["delta"] = 0.1
+        policy_loss_fn = policy_loss_fn_cls(**policy_loss_fn_args)
+        loss, metrics = policy_loss_fn(log_prob=self.logprob, **self.input_data.batch)
+        ppo_loss_masked = torch.tensor(0.22175675630569458)
+        pg_clipfrac = torch.tensor(0.3541666567325592)
+        ppo_kl = torch.tensor(-0.21663446724414825)
+        pg_clipfrac_lower = torch.tensor(0.0625)
+        masked_tokens = torch.tensor(0.16666666666631944)
+        mean_sequence_kl = torch.tensor(-0.21027061343193054)
+        self.assertTrue(torch.allclose(loss, ppo_loss_masked))
+        self.assertTrue(torch.allclose(torch.tensor(metrics["pg_clipfrac"]), pg_clipfrac))
+        self.assertTrue(torch.allclose(torch.tensor(metrics["ppo_kl"]), ppo_kl))
+        self.assertTrue(torch.allclose(torch.tensor(metrics["pg_loss"]), ppo_loss_masked))
+        self.assertTrue(
+            torch.allclose(torch.tensor(metrics["pg_clipfrac_lower"]), pg_clipfrac_lower)
+        )
+        self.assertTrue(
+            torch.allclose(torch.tensor(metrics["seq_mask/masked_tokens"]), masked_tokens)
+        )
+        self.assertTrue(
+            torch.allclose(torch.tensor(metrics["seq_mask/mean_sequence_kl"]), mean_sequence_kl)
+        )
+
     def test_sapo_policy_loss(self):
         policy_loss_fn_cls = POLICY_LOSS_FN.get("sapo")
         policy_loss_fn_args = policy_loss_fn_cls.default_args()

trinity/algorithm/policy_loss_fn/ppo_policy_loss.py

@@ -21,6 +21,8 @@ def __init__(
         clip_range_high: Optional[float] = None,
         clip_ratio_c: float = 3.0,
         loss_agg_mode: Optional[str] = "token-mean",
+        enable_sequence_masking: bool = False,
+        delta: float = 0.1,
     ) -> None:
         super().__init__(backend=backend)
         if clip_range_low is None:
@@ -36,6 +38,8 @@ def __init__(
         assert self.clip_range_low is not None, "clip_range_low must be specified."
         assert self.clip_range_high is not None, "clip_range_high must be specified."
         self.loss_agg_mode = loss_agg_mode
+        self.enable_sequence_masking = enable_sequence_masking
+        self.delta = delta
 
     def __call__(  # type: ignore
         self,
@@ -51,6 +55,33 @@ def __call__(  # type: ignore
         ratio = torch.exp(negative_approx_kl)
         ppo_kl = masked_mean(-negative_approx_kl, action_mask)
 
+        # Compute sequence masking if enabled
+        sequence_mask = torch.ones_like(advantages)
+        if self.enable_sequence_masking:
+            # Compute sequence-level KL divergence: mean KL per sequence
+            # Shape: (batch_size, seq_len) -> (batch_size,)
+            kl_per_token = -negative_approx_kl  # KL divergence per token
+            sequence_kl = (kl_per_token * action_mask).sum(dim=-1) / (
+                action_mask.sum(dim=-1) + 1e-10
+            )
+
+            # Create mask: mask out tokens with negative advantages when sequence KL is high
+            # Token-level advantage check: (batch_size, seq_len)
+            has_negative_advantage = advantages < 0
+            # Sequence-level KL check: (batch_size,) -> (batch_size, 1) -> (batch_size, seq_len)
+            exceeds_kl_threshold = (sequence_kl > self.delta).unsqueeze(-1).expand_as(advantages)
+            # Mask tokens that are both negative advantage AND in high-KL sequences
+            should_mask = has_negative_advantage & exceeds_kl_threshold
+            sequence_mask = (~should_mask).float()
+
+            metrics_seq_mask = {
+                "seq_mask/masked_tokens": should_mask.float().sum().item()
+                / (action_mask.sum().item() + 1e-10),
+                "seq_mask/mean_sequence_kl": sequence_kl.mean().detach().item(),
+            }
+        else:
+            metrics_seq_mask = {}
+
         pg_losses1 = -advantages * ratio
         pg_losses2 = -advantages * torch.clamp(
             ratio, 1.0 - self.clip_range_low, 1.0 + self.clip_range_high  # type: ignore
@@ -66,13 +97,19 @@ def __call__(  # type: ignore
             torch.gt(clip_pg_losses1, pg_losses3) * (advantages < 0).float(), action_mask
         )
         pg_losses = torch.where(advantages < 0, clip_pg_losses2, clip_pg_losses1)
+
+        # Apply sequence mask to the losses
+        if self.enable_sequence_masking:
+            pg_losses = pg_losses * sequence_mask
+
         pg_loss = aggregate_loss(pg_losses, action_mask, loss_agg_mode=self.loss_agg_mode)
         metrics = {
             "pg_clipfrac": pg_clip_frac.detach().item(),
             "pg_clipfrac_lower": pg_clipfrac_lower.detach().item(),
             "ppo_kl": ppo_kl.detach().item(),
             "pg_loss": pg_loss.detach().item(),
         }
+        metrics.update(metrics_seq_mask)
         return pg_loss, metrics
 
     @classmethod
@@ -81,4 +118,6 @@ def default_args(cls) -> Dict:
             "clip_range": 0.2,
             "clip_ratio_c": 3.0,
             "loss_agg_mode": "token-mean",
+            "enable_sequence_masking": False,
+            "delta": 0.1,
         }